Abstract
Introduction: Electronic medical records (EMR) maintained in primary care in the
UK, and collected and stored in EMR databases offer a world-leading resource for observational clinical research. We aimed to profile one such database, the
Optimum Patient Care Research Database (OPCRD).
Methods and Participants: The OPCRD, incepted in 2010, is a growing primary care EMR database collecting data from 992 general practices within the UK. It covers over 16.6 million patients across all four countries within the UK, and is broadly representative of the UK population in terms of age, sex, ethnicity and socioeconomic status. Patients have a mean duration of 11.7 years follow-up (SD 17.50), with the majority having key summary data from birth to last data entry. Data for the OPCRD is collected incrementally monthly, and extracted from all of the major clinical software systems used within the UK and across all four coding systems (Read version 2, Read CTV3, SNOMED DM+D and SNOMED CT codes). Via quality improvement programmes provided to GP surgeries, the OPCRD also includes patient reported outcomes from a range of disease specific validated questionnaires, with over 66,000 patient responses on asthma, COPD and COVID-19. Further bespoke data collection is possible by working with GP practices to collect new research via patient-reported questionnaires.
Findings to date: The OPCRD has contributed to over 96 peer-reviewed research
publications since its inception encompassing a broad range of medical conditions including Covid-19.
Conclusions:
The OPCRD represents a unique resource with great potential to support epidemiological research, from retrospective observational studies through to
embedded cluster randomized trials. Advantages of OPCRD over other EMR databases are its large size and UK-wide geographical coverage, the availability of up-to-date patient data from all major GP software systems, and the unique
collection of patient-reported information on respiratory health.
UK, and collected and stored in EMR databases offer a world-leading resource for observational clinical research. We aimed to profile one such database, the
Optimum Patient Care Research Database (OPCRD).
Methods and Participants: The OPCRD, incepted in 2010, is a growing primary care EMR database collecting data from 992 general practices within the UK. It covers over 16.6 million patients across all four countries within the UK, and is broadly representative of the UK population in terms of age, sex, ethnicity and socioeconomic status. Patients have a mean duration of 11.7 years follow-up (SD 17.50), with the majority having key summary data from birth to last data entry. Data for the OPCRD is collected incrementally monthly, and extracted from all of the major clinical software systems used within the UK and across all four coding systems (Read version 2, Read CTV3, SNOMED DM+D and SNOMED CT codes). Via quality improvement programmes provided to GP surgeries, the OPCRD also includes patient reported outcomes from a range of disease specific validated questionnaires, with over 66,000 patient responses on asthma, COPD and COVID-19. Further bespoke data collection is possible by working with GP practices to collect new research via patient-reported questionnaires.
Findings to date: The OPCRD has contributed to over 96 peer-reviewed research
publications since its inception encompassing a broad range of medical conditions including Covid-19.
Conclusions:
The OPCRD represents a unique resource with great potential to support epidemiological research, from retrospective observational studies through to
embedded cluster randomized trials. Advantages of OPCRD over other EMR databases are its large size and UK-wide geographical coverage, the availability of up-to-date patient data from all major GP software systems, and the unique
collection of patient-reported information on respiratory health.
| Original language | English |
|---|---|
| Pages (from-to) | 39-49 |
| Number of pages | 11 |
| Journal | Pragmatic and Observational Research |
| Volume | 2023 |
| Issue number | 14 |
| Early online date | 27 Apr 2023 |
| DOIs | |
| Publication status | Published - 27 Apr 2023 |
Keywords
- primary care
- electronic health records
- medical records
- datasets
- demography
- health outcomes